.png){kind=icon}
返回
Mutation effect estimation on protein-protein interactions using deep contextualized representation learning
.png){kind=icon}
OA
AI
DOI：10.1093/nargab/lqaa015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein-protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas.
Keyword:
BINDING-AFFINITY
CD-HIT
PREDICTION
GENERATION
STABILITY
DATABASE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
G
IF:
7.9
论文数:
1.5K
被引数:
6.0K
机构
引用论文
.png){kind=icon}
分享收藏
Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health蛋白质-蛋白质界面的突变: 大表面上的小变化对人类健康有很大影响OAAI
OAAI分享收藏Targeting the MDM2-p53 Protein-Protein Interaction for New Cancer Therapy: Progress and ChallengesOAAI
OAAI分享收藏
查看更多内容